The present invention relates to a method and apparatus for minimizing blurring of an image in a radiation imaging system, more particularly a method which combines inverse filtering and suppression of noise at frequencies, where the signal-to-noise ratio (SNR) is low to generate a high resolution signal appropriate for driving a display and analysis module and which employs a filter having a frequency response which combines inverse filtering and noise regularization and which is controlled by a single parameter such that the noise regularization decrease the frequency response of the filter as the frequency of the signal increases.
High resolution radiation imaging systems are well-known in the art, as exemplified by U.S. Pat. No. 5,340,988 assigned to General Electric Company, the assignee of the instant application. Such radiation imaging systems are typically used in medical imaging, in which radiation passing through, or emanating from, a patient's body is used to visualize objects or materials within the body. Digital imaging systems typically use a two dimensional array of photosensors to convert radiation into an image where intensity is proportional to the incident radiant energy. The incident radiation can either be converted first to optical photons by a scintillator and then to an electrical signal or the incident radiation can be converted directly into an electrical signal. The signal from a photosensor represents an individual pixel in the digital image. Medical imaging devices exhibit high spatial frequency response, short image acquisition time and high detective quantum efficiency.
In order to improve the resolution of the resultant image, the above-identified U.S. patent provides a radiation imaging system having a photosensor pixel array that is movable to a plurality of sequential imaging positions according to a predetermined imaging cycle and an image processor electrically coupled to receive image data signals generated by respective ones of the photosensor pixels in the array. The image processor stores the image data signals generated during an imaging cycle as an unfiltered data set, the image processor further comprising a deblurring filter adapted to selectively filter the unfiltered data set to generate a fine resolution data set.
The sequential imaging positions are selected such that during one imaging cycle the aliasing errors are cancelled in the generation of the fine resolution data set.
Typically the plurality of sequential imaging positions comprises four imaging positions, the positions being respectively disposed such that the distance between adjacent imaging positions along a selected axis of movement for the photosensor pixel array are such that, in each respective imaging position, each photosensor pixel in the imager is centered on only one of the respective areas represented in each pixel element in the output fine resolution array in each imaging position. Typically, the distance between adjacent imaging positions along the selective axis of movement is substantially one-half the pitch of the photosensor pixel array, the pitch being the distance between centers of adjacent pixels.
The limiting factor in the resolution of a digital imaging system is the pixel pitch, the distance between centers of adjacent photosensor pixels in the array. The smaller the pixel pitch, the higher are the spatial frequencies that can be unambiguously detected by the array, the highest unambiguous frequency which can be detected being known as the Nyquist frequency. Frequencies above the Nyquist frequency will appear at lower frequencies in the digital image. Such phenomenon is referred to as aliasing and results in distortion of the high frequency components of the original signal. As a practical matter, the pixel pitch of the pixel array and, hence the resolution, are limited by the costs and the difficulty in manufacturing photosensor arrays having extremely small pixel pitches.
The aforementioned technique is known as oversampling and is a known method of obtaining a higher resolution image using a lower resolution detector. When a photosensor array is used in an oversampling mode, multiple exposures are taken with the same photosensor array positioned at different locations. A high resolution image can be obtained by sequentially moving the array by one-half the pixel pitch to four different imaging locations and interleaving respective image data to form a high resolution image. As illustrated in FIG. 1, a first image is taken at 1, the photosensor array is moved to the right by a distance equal to one-half of the pixel pitch and a second image is taken at 2. Then, the array is moved in an upward direction for a third image at 3 and, finally, moved to the left for the last image at 4. In each instance, the distance moved by the array is equal to one-half of the pixel pitch. The four images are then combined into an oversampled image by interleaving the pixels according to their relative positions. The oversampled image has four times the number of pixels as a single image which essentially reduces the pixel pitch by one-half, thereby doubling the resolution of the imaging system. Although four images have been used as an example to demonstrate the fundamentals of oversampling, it is to be understood that more, or less, than four images may be utilized. If the photosensor array is moved n times in a given direction, the oversampled image will alter the pixel pitch accordingly.
The oversampled image is degraded during acquisition by the system modulation transfer function, with main causes being the pixel's finite aperture size and the frequency selectivity of the scintillator. This degradation can be modeled as a linear system and a transfer function can be mathematically derived, or experimentally measured, to form an overall blurring function. Given an overall, or a partial, blurring function, the interleaved image can be filtered in such a way as to undo the effects of the degradation of the image. The interleaved image also contains additive noise that must taken into account, since disregarding the noise while filtering can severely amplify the high frequency noise and further degrade the image.
In the image processing field, the blurring problem described above falls under the general heading of image restoration. Image restoration attempts to reduce or eliminate degradation of an image to be displayed by using information about the degradation and the image acquisition signals. Typically, in this environment, the image acquisition signal is degraded by two factors, additive random noise and blurring. Techniques in image processing to combat these effects include Wiener filtering, Kalman filtering, regularized inverse filtering and iterative restoration algorithms.
For images degraded only by noise, image processing techniques to deal with additive noise are based on Wiener filtering. Wiener filtering provides an optimal linear minimum mean square estimate of the original image. The Wiener filter requires knowledge of both the signal and the noise spectra for specifying the filter's frequency response. The result of the Wiener filtering is to preserve the high signal-to-noise ratio frequency components, while attenuating the lower signal-to-noise frequency components. Typically, Wiener filters tend to be lowpass filters.
Techniques to counteract the effect of a known blurring function are referred to as inverse filtering, deblurring, or deconvolution. These techniques produce a filter whose frequency response, H.sub.inv (v.sub.x, v.sub.y), is the inverse of the blurring function, or: ##EQU1## where v.sub.x and v.sub.y are the spatial frequencies and B(v.sub.x,v.sub.y) is the Fourier transform of the blurring function. Typically, the blurring function has a lowpass frequency spectrum.
An inherent problem with inverse filtering is that the frequency response becomes very large at frequencies where the Fourier transform of the blurring function is very small. If noise is present at high frequencies, which is normally the case, the filter will greatly emphasize or amplify the noise at frequencies with a low signal-to-noise ratio. As a result, inverse filtering alone can adversely impact the quality of the displayed image.
To account for additive noise and blurring simultaneously, the inverse filtering must be modified, or regularized, to reduce the noise enhancement at frequencies where the frequency response becomes too large or the signal-to-noise ratio is too small. Wiener filtering and inverse filtering can be combined by cascading the two filters to produce an optimal linear estimator minimizing the mean squared error for a signal degraded by blurring and additive noise. However, this technique requires knowledge of the noise and signal spectra which are often not readily available.
Thus, a need exists for a filter and method for deblurring the image data or acquisition signals generated in the image detector assembly during an imaging cycle of a radiation imaging system which accounts for the above criteria when the noise and signal spectra are not accurately known.